Sustainability and Goal Fitness Index for the Analysis of Sustainable

Development Goals: A Methodological Proposal

Sanny González

, Gabriel Pereira

1,2,3 b

and Arturo González

Universidad Nacional de Asunción, Facultad Politécnica, Grupo de Investigación en Sistemas Energéticos

(FPUNA-GISE), San Lorenzo, Paraguay

Transición Energética y Desarrollo Sostenible (TrEnDS), Asunción, Paraguay

Universidad Americana, Facultad de Ciencias Económicas y Administrativas, Lab-iDi, Asunción, Paraguay

Keywords: Sustainable Development Goals (SDGs), Economic Fitness, Complexity, Sustainability Fitness Index (SFI),

Goal Fitness Index (GFI), Goal Achievement Capability (GAC), Sustainability.

Abstract: The Sustainable Development Goals (SDGs) were adopted in September 2015 by the 193 member states of

the United Nations (UN), which include 17 goals, 169 targets and 244 indicators, as an attempt to radically

change the approach of the Sustainable Development Goals. Millennium Development (MDG). Since the

adoption of the 2030 Agenda, the scientific community has increased its interest in the evaluation, analysis,

and evaluation of the interrelationships between the SDGs, proposing different approaches and using a

diversity of methodological tools for the interactions of the SDGs. This research proposes a methodology that

takes advantage of the concepts of Economic Fitness for the creation of a Sustainability Fitness Index (SFI)

for the countries and a Goal Fitness Index (GFI) for each SDG. These indices are intended to provide a tool

to analyze the interrelationships between the Sustainable Development Goals in such a way that they offer a

new approach to address the capacities of the countries and the fulfillment of the SDGs. The results of the SFI

are a first attempt to identify development priorities aligned with the SDGs in each country, based on their

available productive capacities, which could help make more efficient use of their limited resources and

increase the achievement of the SDGs.

1 INTRODUCTION

The 2030 Agenda represents a new era in the

worldwide challenge of achieving some of the most

ambitious objectives for the humanity, setting a “plan

of action for people, planet and prosperity” that must

be achieved within 15 years (2015-2030) (UN, 2015).

In this pathway towards sustainability, the

countries have experienced several implementation

challenges, including limited resources (economic,

human, infrastructure, etc.), highly complex network

of interactions between SDGs, and lack of alignment

between national development plans and the 2030

Agenda. (Lack of policy coherence; policy vs politics).

In the last few years, the countries have sent their

Voluntary National Reviews (VNRs) to the High-

Level Political Forum on Sustainable Development of

the United Nations, sharing their experiences and

https://orcid.org/0000-0002-8385-2852

https://orcid.org/0000-0001-9966-6715

https://orcid.org/0000-0001-5672-3679

results in the implementation of the SDGs at the

national level (UN, 2016). The learnings from these

experiences have enhanced the importance of

improving the understanding of the nature and impact

of the interlinkages between the different SDGs at the

national level, considering their universal and

integrated design.

As many experts have underlined, in this global

scenario and facing the complexity and universality

of the SDGs, a priority setting for the implementation

of the 2030 Agenda is recommended (Pereira et al,

2021; Allen et al., 2018; Allen et al., 2018a; Weitz et

al., 2018; Zelinka & Amadei, 2019; McGowan et al.,

2018), in order to: improve the qualitative and

quantitative understanding on SDGs interactions;

identify direct and indirect effects of SDGs

interactions; detect patterns on SDGs interactions;

identify critical goals and targets (central nodes) in

González, S., Pereira, G. and González, A.

Sustainability and Goal Fitness Index for the Analysis of Sustainable Development Goals: A Methodological Proposal.

DOI: 10.5220/0011122400003197

In Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2022), pages 105-115

ISBN: 978-989-758-565-4; ISSN: 2184-5034

105

the SDG network; and secondary analyses to increase

synergies and avoid trade-off in the implementation

of the 2030 Agenda.

This work follows the ideas presented in (Pereira

et al., 2021) who presented a paper that studies the

interactions between countries and their compliance

with the SDGs from the point of view of complex

systems, based mainly on the theory of economic

complexity proposed by (Haussman et al., 2014).

The aim of this study is to propose a new

methodological approach for the analysis of the SDG

interlinkages and the progress of the countries in the

implementation of the 2030 Agenda, based on their

accumulated sustainability capabilities measured

using economic fitness and network theory

(Tacchella et al., 2012; Cristelli et al., 2013;

Tacchella et al., 2013; Pugliese, Zaccaria &

Pietronero., 2016).

This paper is organized as follow: first, in Section

II a brief account of state-of-the-art literature on

Sustainable Development Goals (SDGs) and SDG

interlinkages analysis from the point of view of

Economic Fitness is made. Then, in Section III the

methodology, based on the economic fitness (from

the point of view of complexity analysis) to evaluate

the SDG interlinkages is explained. Third, in Section

IV, we show the results and discussion of our

analyses, including the interpretation of the findings.

Finally, in Section V, the conclusions are

presented.

2 LITERATURE REVIEW

The UN Sustainable Development Goals (SDGs),

adopted in September 2015 in the document called

“Transforming our world: the 2030 Agenda for

Sustainable Development” set the structure of the

SDGs, including its 17 goals, 169 targets and 244

indicators, as an attempt to change the approach from

the top-down agenda of the Millenium Development

Goals (MDGs) to the bottom-up agenda of the SDGs.

This new approach should improve the adoption of

the “indivisible and integrated” 2030 Agenda,

focusing on the 3 dimensions of the sustainable

development: social, economic, and environmental

(UN, 2015).

As mentioned before, the scarcity of resources is

one of the main challenges that the countries must

face in their way towards sustainability. In this

context, and according to (UNCTAD, 2014),

achieving the 2030 Agenda will require not only

political commitment, but also important global

investments of approximately 5-7 trillion USD per

year (2015-2030), which already presents important

gaps.

Then, from the perspective of the complexity of

the interactions in the SDG`s network, the evidence

from the VNRs reveal the need of improving the

understanding of the interlinkages between goals,

targets, and indicators in the system, to take

advantage of the synergies and to improve policy

coherence and alignment with the national

development plans (UN, 2016; Allen et al., 2018;

Allen et al., 2018a; Weitz et al., 2018; Pereira et al.

2021).

2.1 The Design & Nature of the SDGs

Since 2016 the scientific community has increased its

interest in the assessment, analysis, and evaluations

of the interlinkages between the SDGs, proposing

different approaches and using a diversity of

methodological tools for SDG interactions.

Moreover, the analysis of SDG interlinkages offers

fundamental information for policymakers, guiding

the decision-making and the policy-design, to balance

the different interests of the country (social,

economic, or environmental).

In this context, the authors have begun to focus

the analysis in the progress of countries in the

accomplishment of the SDGs, through rankings (by

goals, targets or indicators), qualitative

methodologies, traffic light approaches, and many

others (Griggs et al., 2017; ICSU, ISSC, 2015; Sachs

et al., 2018; Schmidt-Traub et al., 2017; Salvia et al.,

2019), in order to identify critical goals and targets

for the sustainable development of the countries.

Nowadays, the report made by (Sachs et al., 2018)

and published annually since 2016 with Bertelsmann

Stiftung and the Sustainable Development Solutions

Network (SDSN), is the reference for evaluating the

progress of countries towards sustainable

development.

The analysis and evaluation of the SDGs is a very

complex task, as it has been already underlined in

several studies (Dargin et al., 2019; Karnib, 2017;

McCollum, et al., 2018), therefore, new

methodologies have been proposed in the last years to

improve our understanding.

In the beginning, the literature on the SDG

interlinkages focused on the study of one-on-one

impact, evaluating the interaction of an SDG with

another goal or development priority (Vladimorova &

Le Blanc, 2016, Alcamo, 2019; Nerini et al., 2017;

Maes et al., 2019).

More recently, the scope has been expanded to the

analysis of the interactions between a set of goals, in

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106

an approach known as the “nexus approach”. Then,

several studies propose different “nexus” and

combinations of set of goals, as for example: water-

energy-food nexus, energy-poverty-climate nexus,

etc. (Liu et al, 2018; Bleischwitz et al, 2018; Dargin

et al., 2019; Karnib, 2017).

As mentioned by (Liu et al., 2018), the nexus

approach facilitates the identification of synergies

between goals, the improvement of policy design and

the implementation of policies. Moreover, the nexus

approach reduces the “silo-thinking” to focus on the

synergies of critical resources and the promotion of

wellbeing (Liu et al., 2018; Bleischwitz et al., 2018;

Dargin et al., 2019; Karnib, 2017).

Authors using the nexus approach underline that

focusing only on the type of interaction is not enough,

and it must also be considered an impact analysis

(direct or indirect) of the interactions (Karnib, 2017).

Recent studies have incorporated semi-

quantitative methodologies with the purpose of

improving the comprehension of the interactions

(synergies and trade-off) in the intricate and complex

SDG network, offering a new perspective in the

analysis and visualization of the different interactions

(i.e. network analysis) (Allen et al, 2018; Allen et al,

2018a; Weitz et al., 2018; Zelinka & Amadei, 2019;

McGowan et al, 2018; Lusseau & Mancini, 2018).

The results of these studies are relevant for

policymakers and stakeholders to comprehend the

nature of the SDG interlinkages and to improve the

SDG priority setting at the national level.

Nevertheless, even if we still have low understanding

of the SDG interactions, the existent literature in this

topic have demonstrated that there are more positive

interactions (synergies) than trade-off in the SDG

network (Weitz et al., 2018; Nerini et al., 2017; Maes

et al., 2019).

Even with its limitations, the analysis of

interactions between SDGs are fundamentally

important for politics and policymakers, considering

that allows the identification of development

priorities for the countries, the validation of strategic

policies through the alignment with the prioritized

SDGs (policy coherence) and the evaluation of

strategies for development at the national level (Allen

et al., 2018a), Le Blanc, 2015; Nerini et al., 2017;

Karnib, 2017; Maes et al., 2019; Griggs et al., 2017).

The challenge of understanding the intricate and

complex SDG network of interactions have been

clearly explained by (Weitz et al., 2018), which have

expressed: “Understanding interactions between

targets requires quite detailed information, but it also

requires the ability to maintain a holistic view of the

system as a whole, since it is possible that one policy

change can change the dynamics of the whole

system”.

2.2 Understanding the SDG

Interlinkages

Considering the universality of SDGs, the diversity of

sectors and stakeholders, the scarcity of resources,

and the complexity of the interactions in the SDG

network, is inevitable and almost obligatory, the

identification of priorities within the SDGs (Allen et

al, 2018; Weitz et al., 2018; McGowan et al., 2018;

Alcamo, 2019; Nilsson et al., 2016; Scherer et al.,

2018; Singh et al., 2018; Pereira et al., 2021). The

selection of priorities within the SDGs, are the

reflection of the strategies and policies that each

country (expressed by its policymakers) has decided

to adopt, considering the level of urgency in each

sector (McGowan et al., 2018).

The study of the SDG interlinkages has rapidly

evolved from the pioneer study of (Le Blanc, 2015),

criticized for the superficiality of the analysis of the

interactions between SDGs and the mapping

visualization. Similarly, (Vladimorova & Le Blanc,

2016) have presented an analysis of official reports

from the United Nations to evaluate the interactions

between education and SDGs, using the wording

reference methodology. Again, as in the previous

study, the results lack of deepness in the analysis of

the SDG interlinkages.

Applying the network approach and reinforcing

the results presented by (Le Blanc, 2015) about the

asymmetry of the interlinkages between the SDGs,

(McGowan et al., 2018) highlight that those

interlinkages are uneven, observing the lack of

connections between critical SDGs as those related to

gender equality, peace, and governance. These

authors have based their analysis on the report from

the (Griggs et al., 2017) and based on the interactions

identified on it from a science-based perspective

(ICSU, ISSC, 2015), they constructed a SDG network

of interactions considering 4 main elements: degree

(number of links per node), strength (total number of

links from a node), closeness (distance with other

nodes in the network and centrality of a node in the

network), betweenness (flow of information through

the network).

Following with the use of the network approach,

(Allen et al., 2018) and (Allen et al., 2018a) have

implemented a network analysis of SDG targets

interlinkages for 22 Arab countries, based on the

assessment scale of (Nilsson et al., 2016) for the

evaluation of the intensity of the interactions (from -

3 to +3), through a cross-impact matrix to identify

Sustainability and Goal Fitness Index for the Analysis of Sustainable Development Goals: A Methodological Proposal

107

synergies, trade-off, and neutral interactions. The

SDG network, obtained through an expert elicitation

process, considers to 2 main network metrics: the

outdegree and closeness centrality. These results are

later used as inputs for the evaluation of policy gaps

and the design of a multi-criteria analysis, helping to

set the development priorities for the Arab region.

Using the same methodology, (Weitz et al., 2018)

have evaluated the interactions between 34 SDG

targets, obtaining results that reinforce the hypothesis

that there are more synergies than trade-off in the

SDG network, but in which the trade-off represents a

serious threat for the accomplishment of the 2030

Agenda worldwide. The SDG network obtained in

this study provides a deeper level of analysis,

showing the directionality of the interactions between

SDG targets, the type of interactions, the intensity of

the influence of targets in the SDG network, and the

clusters of SDG targets in the network.

Recently (Lusseau & Mancini, 2018) analyzed

how the interactions of the SDGs, at the goal and

target levels, vary according to the level of income of

countries. The results show the existence of unstable

networks, composed by antagonistic subgroups,

where the identification of development of priorities

in each country is needed.

2.3 SDG Priorization & Economic

Complexity

The study from (El-Maghrabi et al., 2018) has set the

foundations for the use of the principles of economic

complexity and the product-space theory in the

challenge of setting priorities within the SDGs, based

on the capabilities of each country. This study, from

the World Bank, has only made a methodology

proposal and offered only a few examples of its

utility, having a very limited scope.

In the same context, (Pereira et al., 2021) broaden

the scope of the methodology proposed by (El-

Maghrabi et al., 2018) and offered a wider

perspective on how countries could use the economic

complexity principles and the product space theory to

set priorities, to rank the SDGs according to their

complexity (Goal Complexity Index), and to rank the

countries according to their performance towards

sustainable development (Sustainability Complexity

Index).

The results from (Pereira et al., 2021) show that

according to the Goal Complexity Index (GCI), the

top 3 of more complex goals in the 2030 Agenda, are

the SDG12 (Responsible Production &

Consumption), SDG13 (Climate Action) and SDG17

(Peace, Governance & Partnerships). In the other

hand, the least complex goals are SDG9 (Industry,

Innovation, and Infrastructure), SDG3 (Health &

Wellbeing) and SDG7 (Energy). In this context, an

optimal strategy for countries could be following the

sustainability complexity path, to fully achieve the

2030 Agenda, advancing from the accomplishment of

less complex goals to more complex goals,

From the perspective of the Sustainability

Complexity Index (SCI), the results show that the

biggest challenges for the accomplishment of the

SDGs mainly remain in Africa and Southeast Asia. In

South America, Bolivia and Venezuela present the

lowest levels of SCI.

It is important to note that the work carried out by

(Pereira et al., 2021) resulted in the inspiration for the

realization of this work and resulted in the

methodological proposal that is presented as an

alternative for the study of sustainable development

objectives in an innovative way.

2.4 Economic Fitness

The Economic Fitness theory proposes a new

algorithm that shows an iterative and non-linear

approach, which makes it possible to efficiently

capture the link formed between the export basket of

different countries and their industrial

competitiveness (Tacchella et al., 2012; Cristelli et

al., 2013; Tacchella et al., 2013). This model has its

initial basis in the construction of a binary matrix of

countries and products (Mcp), which represents the

export basket of each country, whose elements are 1

if country "c" exports product "p" with revealed

comparative advantage and 0 otherwise. This method

consists of coupled nonlinear maps, and in each

iteration new information is added.

Therefore, the general idea of the algorithm

proposed in the Economic Fitness theory lies in

defining an iteration process for the fitness of the

countries (F

) with the complexity of the products

), and then obtaining the values of the

convergence. In the case of F

, it is appropriate that it

be proportional to the sum of the exported products

weighted by their complexity Q

For the case of Q

it becomes less intuitive,

because, in a first approximation, the complexity of a

product is inversely proportional to the number of

countries that export it. But in each iteration more

information is added considering that, if a country has

a high level of Fitness, the weight is reduced to limit

the complexity of a product, on the other hand,

countries with low Fitness contribute more and

tended to limit the complexity of the products

(Tacchella et al., 2012; Cristelli et al., 2013;

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108

Tacchella et al., 2013; Pugliese, Zaccaria &

Pietronero., 2016). These ideas are summarized in the

iteration of the following equations:

⎩

⎪

⎨

⎪

⎧





()

= M





()







()

∑





()



→

⎩

⎪

⎨

⎪

⎧



()





()

〈





()

〉





()





()

〈





()

〉



Where:

n=Index of iteration.

c=Total number of countries.

p=Total number of products.



=Fitness of the country "c".



=Product Complexity "p".



=Product − Country Logical Matrix.

𝑂𝑏𝑠.: F





and Q





corresponding to the

normalization

Since this theory proposes that less complex

exporters make a dominant contribution to product

complexity, nonlinearity is a fundamental

mathematical property that is unavoidable in view of

the problem of economic diversification (Cristelli et

al., 2013). For the definition of the complexity of the

products, the sum in the denominator is strongly

dominated by the countries that have a lower Fitness

measure. Another issue that must be considered when

considering the product complexity denominator is

that, as the total number of countries that export that

specific product increases, this means that the

complexity of the products decreases, considering

thus the ubiquity of the product.

To establish the M

Matrix, which allows the

calculations of the Economic Fitness, it is necessary

to consider the Revealed Comparative Advantage.

The definition of Revealed Comparative Advantage

(RCA) proposed by Balassa (1965), is used to make

countries and products comparable, since it represents

the exports of products by country. This index

establishes that a country has revealed a comparative

advantage in a product if it exports more than the rest

of the world, in which case the RCA index adopts a

value equal to or greater than one; if it is less than one,

it indicates the opposite. It is formally defined as:

𝑅𝐶𝐴









Where:



=Exports o

the countr

"c" o

the product "p".



=Total Exports of the country "c".



=Total World Exports o

the product "p".



=Total World Exports o

the year

(

All Products

)

This measure makes it possible to build a matrix

that connects each country with the products it

manufactures. The entries in the matrix are 1 if the

export of the product in each country with Revealed

Comparative Advantage is greater than or equal to 1,

and 0 otherwise. We formally define this as the M

(Haussman et al., 2014) matrix, as:

𝑀



=

1, if RCA



≥1

0, otherwise

3 METHODOLOGY

3.1 Methodological Design:

Sustainability and Goal Fitness

Index

This research proposes a methodology that takes

advantage of the concepts of Economic Fitness for the

creation of a Sustainability Fitness Index (SFI) of

the countries and a Goal Fitness Index (GFI) for

each SDGs. These indices are intended to provide a

tool to analyze the interrelationships between the

Sustainable Development Goals in such a way as to

offer a new approach for addressing the capabilities

of the countries and the fulfilment of the SDGs.

To achieve the implementation of the proposed

methodology, two fundamental steps are required.

The first step is to identify the SDG compliance

capabilities of each of the study countries, like the use

of the RCA index proposed by Balassa (1966); and in

a second step, perform the calculations of the SFI and

the GFI based on the mathematical models proposed

by Tacchela et al., (2012).

Step 1: Goal Achievement Capability (GAC)

Each country is responsible for voluntarily reporting

its progress in terms of compliance with the different

SDGs. Each of the 17 Sustainable Development

Goals requires specific capabilities to be achieved.

Although each country is different and has its own

challenges to achieve the goals, however, the

capabilities required for their achievement will

possibly be very similar (at least to a great extent).

Then, considering the concepts of comparative

advantages, an index based on the Goal

Achievement Capability (GAC) is proposed, which

will indicate the relationship between the SDGs

(achieved) and the countries, establishing a

country/goal matrix (M

) like the proposal in Pereira

et al., (2021).

To determine the value of the Goal Achievement

Capability, we propose the use of data obtained from

Sustainability and Goal Fitness Index for the Analysis of Sustainable Development Goals: A Methodological Proposal

109

the public database from the Sustainable

Development Report 2019 proposed by Sachs et al.,

(2019). In this database, a qualitative evaluation is

presented, based on the performances reported by the

countries in each SDG, where we can observe a 4-

colour scale: Green = Goal Achievement; Yellow =

Challenges Remain; Orange = Significant Challenges

and Red = Major Challenges.

Given the scale, we propose that for any SDG that

presents a performance colour other than "Red", it

will be considered that the country has the minimal

capabilities to meet the SDG. So, it is represented by

the following equation:

𝐺𝐴𝐶



=

1𝑖𝑓 𝐺𝑜𝑎𝑙≠𝑅𝑒𝑑

0𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Once the GAC value is obtained for each country

and for each goal, a logical matrix of countries by

goals is created, which we call M

, where, for this

model based on the available data on compliance with

the SDGs, it is necessary to M

= GAC

Step 2: Calculation of the Sustainability Fitness Index

and the Goal Fitness Index

Once the capabilities to meet the goals of each SDG

for each country have been identified, the

mathematical models used in the Theory of Economic

Fitness [Tacchella et al., 2012] (See Section 2.4) for

the analysis of the SDGs are extrapolated. The

following equations are used:

⎩

⎪

⎨

⎪

⎧

SFI





()

=  M



GFI



()



GFI





()

∑



SFI



()



→

⎩

⎪

⎨

⎪

⎧

SFI



()

SFI





()

〈

SFI





()

〉



GFI



()

GFI





()

〈

GFI





()

〉



Where:

n=Index of iteration.

c=Total number of countries.

g=Total number of goals.

SFI



=Sustainability Fitness of the Country "c".

GFI



=Goal Complexity of the Goal "g".



=Country − Goal Logical Matrix.

Obs.: SFI





and GFI





corresponding to the

normalization

3.2 Methodological Steps

A work based on structured methodology in 5 well-

defined steps was carried out.

Data used for the study: https://bit.ly/34YTk0B

Step 1: Identification of Secondary Databases

All data used for this study were obtained from the

following secondary sources:

• SDG compliance data they were obtained from

the public database from the Sustainable

Development Report 2019 proposed by Sachs et

al., (2019)

• Socio-Economic Data: Datos.bancomundial.org,

URL: https://datos.bancomundial.org/indicator/

NY.GDP.PCAP.CD

It should be noted that the data used for the study

is available to interested parties

Step 2: Design of the Complexity Fitness

Mathematical-Computational Model for the Analysis

of the SDGs

The mathematical-computational model was created

based on the Economic Fitness models proposed by

Tacchela et al., (2012) and runs were made using

proprietary models in the software MatLab

. In case

any interested party requires the models, they can

request it from the authors without any

inconvenience.

The scope of the study covered a total of 191

countries. The countries Haiti and Somalia were not

considered for this study because they have not

registered any SDG for which they have a GAC

(GAC

= 1), which does not allow an adequate

analysis for the proposed model.

On the other hand, the convergence of the model

when considering the SFI occurs on average at

iteration 24, and when considering the GFI it occurs

on average at iteration number 25, in both indices a

𝑡𝑜𝑙𝑒𝑟𝑎𝑛𝑐𝑒=10



how to stop point for iterations.

Step 3: Validation of the Results of the Sustainability

Fitness Index

Validations were performed from two positions. The

first corresponds to a comparison between the results

obtained under this proposed model vs the results

obtained by Pereira et al., (2021). On the other hand,

some correlations were made with other known socio-

economic indices to identify some correlation and

thus analyze its implications.

Step 4: Results Analysis

Descriptive comments were made on the results

obtained from the model, in addition to the results of

the validations carried out.

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Step 5: Conclusions and Recommendations

After the analysis of the results, a conclusion on the

methodological proposal is presented, as well as a

critical review of the results and the model presented.

4 RESULTS

Results related to SFI and GFI were obtained, in

addition to the validations carried out for the model.

In terms of the SFI, results were obtained for 191

countries, where their sustainability capabilities can

be inferred based on compliance with the SDGs.

Figure 1 shows graphically the general results of the

SFI.

Figure 1: Worldwide SFI 2019.

The Figure 1 show the results SFI through heat

map for the year 2019, where warmer colors reflect

lower levels of sustainability fitness. Then, from

Figure 1, the biggest challenges for the

accomplishment of the SDGs mainly remain in Africa

and Southeast Asia. In the same context, the biggest

challenge in South America seems to be in Bolivia,

Chile, Venezuela, and Ecuador. Nevertheless, from

the results of the SFI we can observe a diversity and

Table 1: Top-10 best performers SFI 2019.

Rank Country Id. SFI

1 Croatia HRV 1,965

2 Canada CAN 1,867

inland FIN 1,867

rance FRA 1,867

5 Sweden SWE 1,867

6 Switzerland CHE 1,827

7 Denmar

DNK 1,827

8 Czech Republic CZE 1,824

9 Serbia SRB 1,824

10 United

dom GBR 1,813

heterogeneity of performances worldwide, with

countries showing a strong path towards sustainable

development and the achievement of the SDGs.

For example, in Table 1 we can observe the list of

the top-10 performers in the SFI, finding mostly

European and high-income countries. In the other

hand, in the list of worst performers in the SFI (Table

2), we mainly find African and low-income countries.

However, further studies are needed to improve our

understanding of the correlation and causality

between performance on the SFI, level of income and

the achievement of the SDGs worldwide.

Table 2: Top-10 worst performers SFI 2019.

Rank Country Id. SFI

184 South Sudan SSD 0,254

185

ola

GO 0,251

186 U

anda UGA 0,242

187

icronesia,

ed. Sts.

SM 0,230

188 Chad TCD 0,203

189

Afg

hanistan

0,190

190

aiti

0,152

191 Central

rican Republic CAF 0,149

192 Con

o, Dem. Rep. COD 0,149

193 Somalia SOM 0,098

Table 3: Ranking of GFI 2019.

Rank Goal GFI 2019

1 Goal 16 2,073

2 Goal 2 1,962

3 Goal 3 1,689

4 Goal 9 1,434

5 Goal 10 1,295

6 Goal 6 1,136

Goal 1 0,984

8 Goal 5 0,851

9 Goal 8 0,806

10 Goal

0,802

11 Goal 14 0,779

12 Goal 4 0,760

13 Goal 11 0,674

14 Goal 1

0,466

15 Goal 15 0,443

16 Goal 13 0,435

Goal 12 0,411

In Table 3 you can see the result obtained for the

GFI. The SDGs that are at the bottom of the ranking

are those for which the least capabilities are required

for their implementation in the countries. On the other

hand, the SDGs with the highest GFI and therefore

located in the first places, correspond to those that are

Sustainability and Goal Fitness Index for the Analysis of Sustainable Development Goals: A Methodological Proposal

111

highly complex, so not many countries have the

capacity to achieve them.

On the other hand, following the results obtained

in the validation process of the SFI 2019, four

correlation analyzes were carried out with: The

GDP

; The Rank of Global Competitiveness Index;

The Rank of Government Effectiveness; The Rank of

Human Development Index for all countries covered

in the study for the year 2019.

Figure 2: Relation between SFI 2019 Vs GDP

2019.

There is a very interesting trend in terms of the

SFI and the level of the GPD

of the countries. It

could be inferred that as the GPD

increases, the SFI

also increases. Therefore, it is an element that could

be important to increase the fitness of countries in

terms of sustainability (See Figure 2).

This behavior is also repeated when the SFI is

subjected to correlation with other indices such as the

Rank of Global Competitiveness Index and the Rank

of Government Effectiveness. Both high indices

imply that the countries are highly competitive and

effective. It is not strange to infer that they have the

capacity to implement programs and public policies,

which would at least make it easier to establish and

comply with plans and actions that allow achieving

sustainability goals. In Figure 3 and Figure 4 the

mentioned behavior can be clearly observed.

Regarding the behavior of the connection

between the SFI and the HDI, an interesting

connection could also be observed. This allows us to

infer a priori that countries with a HIGH index may

have greater capacities to achieve sustainability

goals. It would be necessary to carry out more

studies and with a greater range of years to obtain

better observations and therefore better conclusions

on this point (See Figure 5).

Figure 3: Relation between Rank of SFI 2019 Vs Rank of

Global Competitiveness Index

2019.

Figure 4: Relation between Rank of SFI 2019 Vs Rank of

Government Effectiveness 2019.

Figure 5: Relation between Rank of SFI 2019 Vs Rank of

Human Development Index

2019.

5 CONCLUSIONS

The methodological approach proposed in this study

aims to guide the policy-design and decision-making

in countries, through the use and consideration of

data, capabilities, comparative advantages, and

COMPLEXIS 2022 - 7th International Conference on Complexity, Future Information Systems and Risk

112

fitness metrics. As in previous studies, the analysis of

the SFI is limited to the availability of data series,

public information, and reliable data on the progress

of the countries in their performances in the different

SDGs. It must be underlined, that the methodology

used for the SFI is limited, because the data from the

SDG Report are not comparable year-by-year.

However, the Sachs et al., (2021) has stablished a

definitive methodology that will allow data

comparability for the following years.

The results of the SFI are a first attempt to identify

development priorities aligned with the SDGs in each

country, based on their available productive

capabilities, which could help to make a more

efficient use of their limited resources and boost the

achievement of the SDGs. Following this path could

help the country countries to accelerate their way

towards sustainable development and to create

synergies within the SDG network.

It is important to highlight that by taking the

Economic Fitness model, applied to the analysis of

the SDGs, it is possible to take advantage of the

virtues to obtain more information about the

capabilities necessary to achieve a goal. This occurs

because the countries that achieve few goals provide

more information, since it can be inferred that the

goals that these countries have achieve with less

capabilities than others and have still managed to

meet them.

For the next steps, we suggest further studies on

the SFI and GFI, to improve the experimentation and

validation of the mathematical model and fitting the

parameters used to define which countries presents

the minimal capabilities to achieve an SDG.

ACKNOWLEDGEMENTS

The authors are very grateful to the Paraguayan

National Council of Science and Technology

(CONACyT) for financial support through the

PRONII Program.

AUTHORS CONTRIBUTIONS

Activities

Methodology

Literature

Review

Mathematic

Model

Data

Manuscript

Calculations

Results

Analysis

S.G. X X X X X

G.P. X X X X

A.G. X X X X

REFERENCES

Alcamo, J., 2019. Water quality and its interlinkages with

the Sustainable Development Goals. Current Opinion in

Environmental Sustainability, 36, pp. 126–140.

Allen, C., Metternich, G., Wiedmann, T., 2018. Prioritising

SDG targets: assessing baselines, gaps and

interlinkages. Sustainability Science.

https://doi.org/10.1007/s11625-018-0596-8

Allen, C., Metternich, G., Wiedmann, T., 2018a. Initial

progress in implementing the Sustainable Development

Goals (SDGs): a review of evidence from countries.

Sustainability Science. https://doi.org/10.1007/s11625-

018-0572-3

Balassa, B. (1965). Trade liberalisation and “revealed”

comparative advantage 1. The manchester school,

33(2), 99-123.

Bleischwitz, R., Spataru, C., VanDeveer, S.D., Obersteiner,

M., van der Voet, E., Johnson, C., Andrews-Speed, P.,

Boersma, T., Hoff, H., van Vuuren, D.P., 2018.

Resource nexus perspectives towards the United

Nations Sustainable Development Goals. Nature

Sustainability, Volume 1, pp. 737-743.

https://doi.org/10.1038/s41893-018-0173-2

Cristelli, M., Gabrielli, A., Tacchella, A., Caldarelli, G., &

Pietronero, L. (2013). Measuring the intangibles: A

metrics for the economic complexity of countries and

products. PloS one, 8(8), e70726.

Dargin, J., Daher, B., Mohtar, R.H., 2019. Complexity

versus simplicity in water energy food nexus (WEF)

assessment tools. Science of the Total Environment,

650, pp. 1566–1575. https://doi.org/10.1016/j.scitote

nv.2018.09.080

Dörgo, G., Sebestyén, V., Abonyi, J., 2018. Evaluating the

Interconnectedness of the Sustainable Development

Goals Based on the Causality Analysis of Sustainability

Indicators. Sustainability 2018, 10, 3766;

doi:10.3390/su10103766

El-Maghrabi, M. H., Gable, S., Osorio Rodarte, I., Verbeek,

J., 2018. Sustainable Development Goals Diagnostics:

An Application of Network Theory and Complexity

Measures to Set Country Priorities. World Bank Group

- Office of the Senior Vice President UN Relations and

Partnerships. Policy Research Working Paper 8481,

WPS8481.

Felipe J, Hidalgo CA. Economic diversification:

implications for Kazakhstan. Development and modern

industrial policy in practice. Issues and country

experiences. Cheltenham UK: Edward Elgar Pub; 2015.

Gusmao, R.G., Leal Filho, W., Gonçalves, O.L., de Mattos,

D.L., Veigas, L., 2018. A literature-based review on

potentials and constraints in the implementation of the

sustainable development goals. Journal of Cleaner

Production, 198, pp. 1276-1288.

https://doi.org/10.1016/j.jclepro.2018.07.102

Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., &

Simoes, A. (2014). The atlas of economic complexity:

Mapping paths to prosperity. Mit Press.

ICSU, ISSC, 2015.Review of the Sustainable Development

Goals: The Science Perspective. Paris: International

Sustainability and Goal Fitness Index for the Analysis of Sustainable Development Goals: A Methodological Proposal

113

Council for Science (ICSU). ISBN: 978-0-930357-97-9

International Council for Science (ICSU), 2017. A Guide to

SDG Interactions: from Science to Implementation

(D.J. Griggs, M. Nilsson, A. Stevance, D. McCollum

(eds)). International Council for Science, Paris. DOI:

10.24948/2017.01

Le Blanc, D., 2015. Towards Integration at Last? The

Sustainable Development Goals as a Network of

Targets. Sustainable Development, 23, pp. 176-187.

DOI: 10.1002/sd.1582

Liu, J., Hull, V., Godfray, H.C.J., Tilman, D., Gleick, P.,

Hoff, H., Pahl-Wostl, C., Xu, Z., Chung, M.G., Sun, J.,

Li, S., 2018Nexus approaches to global sustainable

development. Nature Sustainability, Volume 1, pp.

466-476. https://doi.org/10.1038/s41893-018-0135-8

Lusseau, D., Mancini, F., 2018. Income-based variation in

Sustainable Development Goal interaction networks.

Nature Sustainability. https://doi.org/10.1038/s41893-

019-0231-4

Karnib, A., 2017. Mapping the direct and indirect

interlinkages across the sustainable development goals:

A qualitative nexus approach. International Journal of

Development and Sustainability, Volume 6, No. 9, pp.

1150-1158.

Llamosas, C., Pereira, G., González, A., Blanco, G., 2021.

Energy and development: exploring the potential of the

energy sector to fulfil development goals in Paraguay.

Población y Desarrollo, pp. 58-68, Vol. 27, No. 52,

Junio 2021. https://doi.org/10.18004/pdfce/2076-

054x/2021.027.52.058

Maes, M., Jones, K., Toledano, M. Milligan, B., 2019.

Mapping synergies and trade-offs between urban

ecosystems and the sustainable development goals.

Environmental Science and Policy, 93, pp. 181 -188.

https://doi.org/10.1016/j.envsci.2018.12.010

McGowan, P.J.K., Stewart, G.B., Long, G., Grainger, M.J.,

2018. An imperfect vision of indivisibility in the

Sustainable Development Goals. Nature Sustainability,

Brief Communication. https://doi.org/10.1038/s41893-

018-0190-1

McCollum, D.L., Gomez, L., Busch, S., Pachauri, S.,

Parkinson, S., Rogelj, J., Krey, V., Minx, J.C., Nilsson,

M., Stevance, A-S., Riahi, K., 2018. Connecting the

sustainable development goals by their energy inter-

linkages. Environmental Research Letters 13.

https://doi.org/10.1088/1748-9326/aaafe3

Moyer, J., Bohl, D., 2019. Alternative pathways to human

development: Assessing trade-offs and synergies in

achieving the Sustainable Development Goals. Futures,

105, pp.199-210. https://doi.org/10.1016/j.futures.20

18.10.007

Nerini, F., Tomei, J., Seng To, L., Bisaga, I., Parikh, P.,

Black, M., Borrion, A., Spataru, C., Castán, V.,

Anandarajah, G., Milligan, B., Mulugetta, Y., 2017.

Mapping synergies and trade-offs between energy and

the Sustainable Development Goals. Nature Energy.

https://doi.org/10.1038/s41560-017-0036-5

Nilsson, M., Griggs, D., Visbeck, M., 2016. Map the

interactions between Sustainable Development Goals.

Nature, Volume 534, pp. 320-322.

Nilsson, M., Chisholm, E., Griggs, D., Howden-Chapman,

P., McCollum, D., Messerli, P., Neumann, B.,

Stevance, A-S, Visbeck, M., Stafford-Smith, M., 2018.

Mapping interactions between the sustainable

development goals: lessons learned and ways forward.

Sustainability Science, 13, pp. 1489-1503.

https://doi.org/10.1007/s11625-018-0604-z

Pereira, G.; González, A. and Blanco, G. (2021).

Complexity Measures for the Analysis of SDG

Interlinkages: A Methodological Approach. In

Proceedings of the 6th International Conference on

Complexity, Future Information Systems and Risk -

COMPLEXIS, ISBN 978-989-758-505-0; ISSN 2184-

5034, pages 13-24. DOI: 10.5220/0010374600130024

Pugliese, E., Zaccaria, A., & Pietronero, L. (2016). On the

convergence of the Fitness-Complexity Algorithm. The

European Physical Journal Special Topics, 225(10),

1893-1911.

Sachs, J., Schmidt-Traub, G., Kroll, C., Lafortune, G.,

Fuller, G., 2018. SDG Index and Dashboards Report

2018. New York: Bertelsmann Stiftung and Sustainable

Development Solutions Network (SDSN).

http://sdgindex.org/reports/2018/

Sachs, J., Schmidt-Traub, G., Kroll, C., Lafortune, G.,

Fuller, G. (2019): Sustainable Development Report

2019. New York: Bertelsmann Stiftung and Sustainable

Development Solutions Network (SDSN).

Sachs, J., Kroll, C., Lafortune, G., Fuller, G., & Woelm, F.

(2021). Sustainable development report 2021.

Cambridge University Press.

Salvia, A., Leal Filho, W., Brandli, L., Griebeler, J., 2019.

Assessing research trends related to Sustainable

Development Goals: local and global issues. Journal of

Cleaner Production, 208, pp. 841-849.

https://doi.org/10.1016/j.jclepro.2018.09.242

Scherer, L., Behrens, P., de Koning, A., Heijungs, R.,

Sprecher, B., Tukker, A., 2018.Trade-offs between

social and environmental Sustainable Development

Goals. Environmental Science and Policy, 90, pp. 65-

72. https://doi.org/10.1016/j.envsci.2018.10.002

Schmidt-Traub, G., Kroll, C., Teksoz, K., Durand-Delacre,

D., Sachs, J., 2017. National baselines for the

Sustainable Development Goals assessed in the SDG

Index and Dashboards. Nature Geoscience, Vol.10, pp.

546-556. DOI: 10.1038/NGEO2985

Singh, G., Cisneros-Montemayor, A.M., Swartz, W.,

Cheung, W., Guy, J.A., Kenny, T-A., McOwen, C.J.,

Asch, R., Geffert, J.L., Wabnitz, C., Sumaila, R.,

Hanich, Q., Ota, Y., 2018. A rapid assessment of co-

benefits and trade-offs among Sustainable

Development Goals. Marine Policy, 93, pp. 223-231.

http://dx.doi.org/10.1016/j.marpol.2017.05.030

Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A., &

Pietronero, L. (2012). A new metrics for countries'

fitness and products' complexity. Scientific reports,

2(1), 1-7.

Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A., &

Pietronero, L. (2013). Economic complexity:

conceptual grounding of a new metrics for global

COMPLEXIS 2022 - 7th International Conference on Complexity, Future Information Systems and Risk

114

competitiveness. Journal of Economic Dynamics and

Control, 37(8), 1683-1691.

United Nations, Synthesis of voluntary national reviews

2016. Department of Economic and Social Affairs,

New York, 2016.

United Nations, Report of the Open Working Group of the

General Assembly on Sustainable Development Goals,

A/68/970: New York, 2014

UNCTAD (United Nations Conference on Trade and

Development): World investment report. 2014

https://unctad.org/en/PublicationsLibrary/wir2014_en.

pdf

United Nations, General Assembly: Transforming our

world: the 2030 Agenda for Sustainable Development,

A/RES/70/1: New York, 2015.

Vladimirova, K., Le Blanc, D., 2016. Exploring Links

Between Education and Sustainable Development

Goals Through the Lens of UN Flagship Reports.

Sustainable Development. DOI: 10.1002/sd.1626

Weitz, N, Carlsen, H., Nilsson, M., Skanberg, K., 2018.

Towards systemic and contextual priority setting for

implementing the 2030 Agenda. Sustainable Sciences,

13, pp. 531-548, https://doi.org/10.1007/s11625-017-

0470-0

Zelinka, D., & Amadei, B. (2019). Systems Approach for

Modeling Interactions Among the Sustainable

Development Goals Part 1. International Journal of

System Dynamics Applications, 8(1), 23–40.

doi:10.4018/ijsda.2019010102

Sustainability and Goal Fitness Index for the Analysis of Sustainable Development Goals: A Methodological Proposal

115